from utils import *
from sklearn.model_selection import train_test_split

df = pd.read_excel('data/DS_assessment.xlsx', sheet_name = 'Data')
df.shape

(5000, 13)

Standardize column names to lowercase, with spaces denoted by _

df = standardize_column_names(df, custom_mapping = {'CCAvgSpending': 'cc_avg_spending', 'InternetBanking': 'internet_banking'})
df.columns

Index(['id', 'age', 'experience', 'income', 'postal_code', 'family_size',
       'cc_avg_spending', 'education', 'mortgage', 'investment_account',
       'deposit_account', 'internet_banking', 'personal_loan'],
      dtype='object')

Perform Sanity Checks

Check for missing values in the datasets

Check if the dataset contains any missing values in its columns.

check_missing_columns(df)

	contains_missing	missing_count	missing_percent
id	False	0	0.00
age	False	0	0.00
experience	False	0	0.00
income	True	20	0.40
postal_code	False	0	0.00
family_size	True	9	0.18
cc_avg_spending	False	0	0.00
education	False	0	0.00
mortgage	False	0	0.00
investment_account	False	0	0.00
deposit_account	False	0	0.00
internet_banking	False	0	0.00
personal_loan	False	0	0.00

income and family_size columns contain missing values. Given that the number and percentage of records with missing income (0.4%) or family_size (0.18%) values is very small and insignificant, let's drop these records.

df.dropna(inplace = True)
df.shape

(4971, 13)

Perform Data Preprocessing

Preprocessing Steps:

1. Cast postal_code to categorical datatype.
2. Convert internet_banking and personal_loan to boolean integers (i.e. NO = 0, YES = 1).
3. Handle outlier/erroneous records.

df.postal_code = df.postal_code.astype('category')
df.dtypes

id                       int64
age                      int64
experience               int64
income                 float64
postal_code           category
family_size            float64
cc_avg_spending        float64
education               object
mortgage                 int64
investment_account       int64
deposit_account          int64
internet_banking        object
personal_loan           object
dtype: object

mapping = {'NO': 0, 'YES': 1}

df.internet_banking = df.internet_banking.map(mapping)
df.personal_loan = df.personal_loan.map(mapping)

print(df.dtypes)
print(f'\nUnique values in internet_banking: {df.internet_banking.unique()}')
print(f'Unique values in personal_loan: {df.personal_loan.unique()}')

id                       int64
age                      int64
experience               int64
income                 float64
postal_code           category
family_size            float64
cc_avg_spending        float64
education               object
mortgage                 int64
investment_account       int64
deposit_account          int64
internet_banking         int64
personal_loan            int64
dtype: object

Unique values in internet_banking: [0 1]
Unique values in personal_loan: [0 1]

Let's take a look at the summary statistics of the data to detect any potential outliers or errorneous data.

df.describe()

	id	age	experience	income	family_size	cc_avg_spending	mortgage	investment_account	deposit_account	internet_banking	personal_loan
count	4971.000000	4971.000000	4971.000000	4971.000000	4971.000000	4971.000000	4971.000000	4971.000000	4971.000000	4971.000000	4971.000000
mean	2505.909877	45.334339	20.100583	73.805472	2.394689	1.937556	56.626836	0.104607	0.060350	0.596862	0.096359
std	1439.973439	11.458282	11.462613	46.074179	1.147091	1.746019	101.813329	0.306077	0.238158	0.490577	0.295112
min	1.000000	23.000000	-3.000000	8.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	1261.500000	35.000000	10.000000	39.000000	1.000000	0.700000	0.000000	0.000000	0.000000	0.000000	0.000000
50%	2506.000000	45.000000	20.000000	64.000000	2.000000	1.500000	0.000000	0.000000	0.000000	1.000000	0.000000
75%	3750.500000	55.000000	30.000000	98.000000	3.000000	2.500000	101.000000	0.000000	0.000000	1.000000	0.000000
max	5000.000000	67.000000	43.000000	224.000000	4.000000	10.000000	635.000000	1.000000	1.000000	1.000000	1.000000

It could be observed that the minimum value for experience is -3. This does not make sense, as it is not possible for anyone to have negative years of professional experience. Let's dive deeper to see how many records contain negative experience values.

neg_exp = df[df.experience < 0]
neg_exp.shape

(51, 13)

There are 51 records with negative experience. Percentage wise, these records account for 1.03% (i.e. 51/4971) of the records, which is a very small insignificant proportion. Hence, let's drop these records as well.

df =  df[df.experience >= 0]
df.shape

(4920, 13)

Perform Feature Engineering

Features to create

1. income_cc_spending_diff: Difference between annual income and annual credit card spending (i.e. income - 12 x cc_avg_spending)
2. cc_spending_income_ratio: Ratio between annual credit card spending and annual income (i.e. 12 x cc_avg_spending / income)
3. mortage_income_diff: Difference between home mortgage value and annual income and (i.e. mortage - income)
4. mortage_income_ratio: Ratio between home mortgage value and annual income (i.e. mortage / income)

df['income_cc_spending_diff'] = df['income'] - 12 * df['cc_avg_spending']
df['cc_spending_income_ratio'] = 12 * df['cc_avg_spending'] / df['income']

df['mortage_income_diff'] = df['mortgage'] - df['income']
df['mortage_income_ratio'] = df['mortgage'] / df['income']

df.head(1)

	id	age	experience	income	postal_code	family_size	cc_avg_spending	education	mortgage	investment_account	deposit_account	internet_banking	personal_loan	income_cc_spending_diff	cc_spending_income_ratio	mortage_income_diff	mortage_income_ratio
0	1	25	1	49.0	91107	4.0	1.6	Undergrad	0	1	0	0	0	29.8	0.391837	-49.0	0.0

Perform Exploratory Data Analysis

Analyse the distribution of target variable, personal_loan

plot_categories_distribution(df, 'personal_loan', width = 800, height = 400)

It could be observed that the dataset is imbalanced in terms of the target variable, personal_loan. The number of customers that do not take up the personal loan (i.e. personal_loan = 0) is more than 9x the number of customers that take up the loan (i.e. personal_loan = 1).

Analyse the probability of personal_loan = 1 given a categorical value

Probability of personal_loan = 1 given postal_code

loan_probs = compute_loan_probability(df, category_col = 'postal_code', ascending = False)
loan_probs.head(10)

	postal_code	personal_loan	probability
819	95135	1	0.666667
921	96008	1	0.666667
501	93311	1	0.500000
723	94705	1	0.500000
289	92056	1	0.500000
175	91129	1	0.500000
589	94108	1	0.500000
11	90016	1	0.500000
53	90059	1	0.500000
469	93022	1	0.400000

The table above suggests that there is some form of correlation between where the customer stays (i.e. postal_code) and whether he will take up the personal loan (i.e. personal_loan). Customers living at certain postal codes are more likely to take up the personal loan.

For example, it could be observed that customers living at 95135 and 96008 postal codes are very likely to take up the personal loan, each having a $\frac{2}{3}$ chance of doing so.

loan_probs.tail(10)

	postal_code	personal_loan	probability
431	92735	1	0.0
403	92661	1	0.0
425	92705	1	0.0
423	92704	1	0.0
421	92703	1	0.0
417	92694	1	0.0
415	92692	1	0.0
413	92691	1	0.0
407	92673	1	0.0
933	96651	1	0.0

On the contrary, customers living at postal codes such as 92735, 92661, 92705 etc, are unlikely to take up the personal loan, each having a zero probability of doing so based on the provided dataset.

plot_category_loan_distribution(df, category_col = 'education')

The plot above suggests that there is some form of correlation between a customer's education level (i.e. education) and whether he takes up the personal loan (i.e. personal_loan).

For instance, it could be observed that undergrads are less likely to take up the personal loan, only having a 0.045 probability of doing so, as compared to graduates and customers with advanced degrees, which have a 0.132 and 0.138 proability of taking up the personal loan correspondingly.

plot_category_loan_distribution(df, category_col = 'investment_account', height = 400)

The plot above suggests a weak correlation between investment_account and personal_loan given that the probability of personal loan take up for customers who have an investment account with the bank (i.e. 0.117) is slightly higher than those without (i.e. 0.095).

plot_category_loan_distribution(df, category_col = 'deposit_account', height = 400)

The plot above suggests a strong correlation between deposit_account and personal_loan. Customers who have a deposit account with the bank (i.e. 0.47 probability) are ~6x more likely to take up the personal loan as compared to customers without (i.e. 0.073 probability).

plot_category_loan_distribution(df, category_col = 'internet_banking', height = 400)

The plot above suggests an extremely weak correlation between internet_banking and personal_loan. Customers who use internet banking and customers who do not use internet banking are equally likely to take up the personal loan, given that the probability of personal loan take up for both groups is about the same at ~0.1.

Analyse the correlation between numerical feature values and personal_loan

px.box(df, x = 'age', color = 'personal_loan')

From the plot above, it could be observed that the age profile for customers who take up the personal loan (i.e. median 45) is similar to that of customers who do not take up the personal loan (i.e. median 46), given the similar median, q1 and q3 for both groups.

Thus it could be surmised that age is unlikely to be an important factor in determining whether a customer will take up the personal loan.

px.box(df, x = 'experience', color = 'personal_loan')

From the plot above, it could be observed that customers who take up the personal loan (i.e. median 20) have similar number of years of professional experience to customers who do not (i.e. median 20), given the similar median, q1 and q3 for both groups.

Thus it could be surmised that years of professional experience is unlikely to be an important factor in determining whether a customer will take up the loan.

px.box(df, x = 'income', color = 'personal_loan')

From the plot above, it could be observed that in general, customers who take up the personal loan tend to have higher annual income. The median annual income for customers who take up the personal loan is 143K whereas the median annual income for those who do not is 59K.

px.box(df, x = 'family_size', color = 'personal_loan')

The plot above suggests that customers who take up the personal loan tend to have larger family size. The median family size for customers who take up the personal loan is 3 while the median family size for those who do not is 2.

px.box(df, x = 'cc_avg_spending', color = 'personal_loan')

From the plot above, it could be observed that customers who take up the personal loan tend to have higher average monthly credit card spending. The median average monthly credit card spending for customers who take up the personal loan is 3.8K whereas the median average monthly credit card spending for customers who do not is 1.4K.

px.box(df, x = 'mortgage', color = 'personal_loan')

From the plot above, it could be observed that customers who take up the personal loan (i.e. median 0) have similar home mortage values as customers who do not (i.e. median 0), given the similar min, median and q1 for both groups.

Thus it could be surmised that a customer's home mortage value is unlikely to be an important factor in deciding whether he will take up the loan.

px.box(df, x = 'income_cc_spending_diff', color = 'personal_loan')

From the plot above, it could be observed that customers who take up the personal loan tend to have a larger disparity between their annual income and annual credit card spending. The median disparity for customers who take up the personal loan is 95K whereas the median disparity for customers who do not is 36.8K.

px.box(df, x = 'cc_spending_income_ratio', color = 'personal_loan')

From the plot above, it could be observed that customers who take up the personal loan tend to have a slightly higher credit card spending to income ratio (i.e. median 0.352) than customers who do not (i.e. median 0.314).

px.box(df, x = 'mortage_income_diff', color = 'personal_loan')

From the plot above, it could be observed that customers who take up the personal loan tend to have a more negative difference between their home mortage value and annual income. The median difference for customers who take up the personal loan is -118K while the median difference for customers who do not is -40K.

px.box(df, x = 'mortage_income_ratio', color = 'personal_loan')

From the plot above, it could be observed that customers who take up the personal loan (i.e. median 0) have a similar home mortage value to annual income ratio to customers who do not (i.e. median 0), given the similar min, median and q1 for both groups.

Thus it could be surmised that a customer's home mortage value to annual income ratio is unlikely to be an important factor in deciding whether he will take up the loan.

Encode categorical features into numerical values

df, encoder_dict = encode_categorical_features(df)

Perform stratified train test split based on target variable, personal_loan

train, test = train_test_split(df, test_size = 0.3, random_state = 0, stratify = df.personal_loan)

train = train.reset_index(drop = True)
test = test.reset_index(drop = True)

print(f'train shape: {train.shape}')
print(f'test shape: {test.shape}')

train shape: (3444, 17)
test shape: (1476, 17)

Perform Nested Stratified K-Fold Cross-Validation

We perform nested stratified k-fold cross-validation to estimate:

• The average model performance on the full train dataset for each model
• The final number of boosting rounds required for training with the full train dataset for each model

How this works

1. We first stratify split our train dataset into N partitions, based on the personal_loan label.
2. We keep 1 partition as the held-out test set and use the remaining N - 1 partitions for model training and validation.
3. We then further stratify split the N - 1 parititions dataset into another K partitions, based on the personal_loan label and perform K-fold cross validation with it. How this works is that we use K - 1 partitions for training and the remaining 1 partition for hyperparameter tuning. We repeat this process iteratively until all K partitions have been used for hyperparameter tuning. From these series of experiments, we estimate the optimal set of hyperparameters to use for training on the full N - 1 partitions dataset.
4. Following which, we train our model with the full N - 1 partitions dataset and the optimal set of hyperparameters.
5. After that, we compute the performance of our model (i.e. trained on N - 1 partitions dataset) on the held-out test set.
6. We repeat steps 2 - 5 iteratively until all N partitions have been used as the held-out test set.
7. Finally, we compute the average test performance of the models based on all N held-out test sets.

Features used for model training

Target Variable:

• personal_loan

Numerical Features:

• age
• experience
• income
• family_size
• cc_avg_spending
• mortgage
• income_cc_spending_diff
• cc_spending_income_ratio
• mortage_income_diff
• mortage_income_ratio

Categorical Features:

• postal_code
• education
• investment_account
• deposit_account
• internet_banking

Model Selection

For the purpose of model comparison and evaluation, two tree-based models, CatBoost and LightGBM were chosen. The reason why tree-based models were chosen is because they tend to deliver state-of-the-art performance on tabular data problems due to the use of bagging and boosting.

CatBoost and LightGBM were chosen because of their:

• Fast training and inference speed
• Support for categorical features
• Support for regularization
• Superior model performance as compared to their other tree-based counterparts

Model Evaluation Metrics

Given the class imbalance, accuracy might not be the most appropriate evaluation metric to use, as it is biased towards the majority class (i.e. personal_loan = 0). Instead, f1 score might be a more appropriate metric, given that it is the harmonic mean of precision and recall, and hence unbiased towards either of the classes.

Precision:

• Measures the loan take up rate
• Precision = $\frac{\text{No of customers correctly predicted by the model to take up the loan}}{\text{Total no of customers predicted by the model to take up the loan}}$
• From a business standpoint, we would like to maximize precision as higher precision means that we are targeting the correct customers (i.e. those that will take up the personal loan) and lowering the costs and efforts spent on the wrong customers (i.e. those that will not take up the personal loan). Thus, the better model is the one with higher precision.

Recall

• Recall = $\frac{\text{No of customers correctly predicted by the model to take up the loan}}{\text{Actual total no of customers that take up the loan}}$
• From a business perspective, we would like to maximize recall as higher recall means that we are targeting a larger proportion of the customers that will take up the loan; i.e. casting a wider net to include more of the customers that will take up the loan. Hence, the better model is the one with higher recall as well.

There is however a trade-off between precision and recall. Improving one usually will come at the expense of the other. For this exercise, we assume that both precision and recall are equally important to the business. In such a case, f1 score is the appropriate metric to use, as it provides a balance between precision and recall.

num_folds = 5

# Factor to scale num_boost_rounds by in model training
# Factor is calculated based on the ratio of full dataset size / dataset size used for training during each fold of cross validation
scaling_factor = num_folds / (num_folds - 1)

threshold = 0.5
label_col = 'personal_loan'
num_features = ['age', 'experience', 'income', 'family_size', 'cc_avg_spending', 
            'mortgage', 'income_cc_spending_diff', 'cc_spending_income_ratio',
            'mortage_income_diff', 'mortage_income_ratio']
cat_features = ['postal_code', 'education', 'investment_account', 'deposit_account', 'internet_banking']
features = num_features + cat_features

Perform Nested Stratified K-Fold Cross-Validation for LightGBM

model_name = 'lightgbm'
stratified_kfold_results = nested_stratified_kfold_cv(train, features, cat_features, model_name, num_folds = num_folds, scaling_factor = scaling_factor)

Outer train shape: (2755, 17)
test shape: (689, 17)
Inner train shape: (2204, 17)
val shape: (551, 17)
[LightGBM] [Warning] Categorical features with more bins than the configured maximum bin number found.
[LightGBM] [Warning] For categorical features, max_bin and max_bin_by_feature may be ignored with a large number of categories.
[LightGBM] [Info] Number of positive: 215, number of negative: 1989
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.323247 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1838
[LightGBM] [Info] Number of data points in the train set: 2204, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.097550 -> initscore=-2.224749
[LightGBM] [Info] Start training from score -2.224749
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Early stopping, best iteration is:
[33]	valid_0's binary_logloss: 0.0718844
Best iteration: 33
Validation f1 score: 0.8653846153846154
Inner train shape: (2204, 17)
val shape: (551, 17)
[LightGBM] [Warning] Categorical features with more bins than the configured maximum bin number found.
[LightGBM] [Warning] For categorical features, max_bin and max_bin_by_feature may be ignored with a large number of categories.
[LightGBM] [Info] Number of positive: 215, number of negative: 1989
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000326 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1854
[LightGBM] [Info] Number of data points in the train set: 2204, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.097550 -> initscore=-2.224749
[LightGBM] [Info] Start training from score -2.224749
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Early stopping, best iteration is:
[38]	valid_0's binary_logloss: 0.0397198
Best iteration: 38
Validation f1 score: 0.8952380952380953
Inner train shape: (2204, 17)
val shape: (551, 17)
[LightGBM] [Warning] Categorical features with more bins than the configured maximum bin number found.
[LightGBM] [Warning] For categorical features, max_bin and max_bin_by_feature may be ignored with a large number of categories.
[LightGBM] [Info] Number of positive: 214, number of negative: 1990
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000168 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1829
[LightGBM] [Info] Number of data points in the train set: 2204, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.097096 -> initscore=-2.229914
[LightGBM] [Info] Start training from score -2.229914
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Early stopping, best iteration is:
[41]	valid_0's binary_logloss: 0.0393219
Best iteration: 41
Validation f1 score: 0.9357798165137615
Inner train shape: (2204, 17)
val shape: (551, 17)
[LightGBM] [Warning] Categorical features with more bins than the configured maximum bin number found.
[LightGBM] [Warning] For categorical features, max_bin and max_bin_by_feature may be ignored with a large number of categories.
[LightGBM] [Info] Number of positive: 214, number of negative: 1990
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000170 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1840
[LightGBM] [Info] Number of data points in the train set: 2204, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.097096 -> initscore=-2.229914
[LightGBM] [Info] Start training from score -2.229914
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Early stopping, best iteration is:
[31]	valid_0's binary_logloss: 0.0501947
Best iteration: 31
Validation f1 score: 0.897196261682243
Inner train shape: (2204, 17)
val shape: (551, 17)
[LightGBM] [Warning] Categorical features with more bins than the configured maximum bin number found.
[LightGBM] [Warning] For categorical features, max_bin and max_bin_by_feature may be ignored with a large number of categories.
[LightGBM] [Info] Number of positive: 214, number of negative: 1990
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.001800 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1847
[LightGBM] [Info] Number of data points in the train set: 2204, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.097096 -> initscore=-2.229914
[LightGBM] [Info] Start training from score -2.229914
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Early stopping, best iteration is:
[51]	valid_0's binary_logloss: 0.0394272
Best iteration: 51
Validation f1 score: 0.9259259259259259
Average best iteration: 38.8
Scaled average best iteration: 48
[LightGBM] [Warning] Categorical features with more bins than the configured maximum bin number found.
[LightGBM] [Warning] For categorical features, max_bin and max_bin_by_feature may be ignored with a large number of categories.
[LightGBM] [Info] Number of positive: 268, number of negative: 2487
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000158 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1945
[LightGBM] [Info] Number of data points in the train set: 2755, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.097278 -> initscore=-2.227845
[LightGBM] [Info] Start training from score -2.227845
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Test f1 score: 0.9333333333333335
Outer train shape: (2755, 17)
test shape: (689, 17)
Inner train shape: (2204, 17)
val shape: (551, 17)
[LightGBM] [Warning] Categorical features with more bins than the configured maximum bin number found.
[LightGBM] [Warning] For categorical features, max_bin and max_bin_by_feature may be ignored with a large number of categories.
[LightGBM] [Info] Number of positive: 215, number of negative: 1989
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000162 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1830
[LightGBM] [Info] Number of data points in the train set: 2204, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.097550 -> initscore=-2.224749
[LightGBM] [Info] Start training from score -2.224749
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Early stopping, best iteration is:
[37]	valid_0's binary_logloss: 0.0322061
Best iteration: 37
Validation f1 score: 0.9333333333333333
Inner train shape: (2204, 17)
val shape: (551, 17)
[LightGBM] [Warning] Categorical features with more bins than the configured maximum bin number found.
[LightGBM] [Warning] For categorical features, max_bin and max_bin_by_feature may be ignored with a large number of categories.
[LightGBM] [Info] Number of positive: 215, number of negative: 1989
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000141 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1818
[LightGBM] [Info] Number of data points in the train set: 2204, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.097550 -> initscore=-2.224749
[LightGBM] [Info] Start training from score -2.224749
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Early stopping, best iteration is:
[39]	valid_0's binary_logloss: 0.0278291
Best iteration: 39
Validation f1 score: 0.923076923076923
Inner train shape: (2204, 17)
val shape: (551, 17)
[LightGBM] [Warning] Categorical features with more bins than the configured maximum bin number found.
[LightGBM] [Warning] For categorical features, max_bin and max_bin_by_feature may be ignored with a large number of categories.
[LightGBM] [Info] Number of positive: 214, number of negative: 1990
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1828
[LightGBM] [Info] Number of data points in the train set: 2204, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.097096 -> initscore=-2.229914
[LightGBM] [Info] Start training from score -2.229914
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Early stopping, best iteration is:
[39]	valid_0's binary_logloss: 0.0685846
Best iteration: 39
Validation f1 score: 0.8888888888888888
Inner train shape: (2204, 17)
val shape: (551, 17)
[LightGBM] [Warning] Categorical features with more bins than the configured maximum bin number found.
[LightGBM] [Warning] For categorical features, max_bin and max_bin_by_feature may be ignored with a large number of categories.
[LightGBM] [Info] Number of positive: 214, number of negative: 1990
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000147 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1829
[LightGBM] [Info] Number of data points in the train set: 2204, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.097096 -> initscore=-2.229914
[LightGBM] [Info] Start training from score -2.229914
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Early stopping, best iteration is:
[40]	valid_0's binary_logloss: 0.0392562
Best iteration: 40
Validation f1 score: 0.9038461538461539
Inner train shape: (2204, 17)
val shape: (551, 17)
[LightGBM] [Warning] Categorical features with more bins than the configured maximum bin number found.
[LightGBM] [Warning] For categorical features, max_bin and max_bin_by_feature may be ignored with a large number of categories.
[LightGBM] [Info] Number of positive: 214, number of negative: 1990
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000387 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1837
[LightGBM] [Info] Number of data points in the train set: 2204, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.097096 -> initscore=-2.229914
[LightGBM] [Info] Start training from score -2.229914
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Early stopping, best iteration is:
[34]	valid_0's binary_logloss: 0.0391605
Best iteration: 34
Validation f1 score: 0.9142857142857143
Average best iteration: 37.8
Scaled average best iteration: 47
[LightGBM] [Warning] Categorical features with more bins than the configured maximum bin number found.
[LightGBM] [Warning] For categorical features, max_bin and max_bin_by_feature may be ignored with a large number of categories.
[LightGBM] [Info] Number of positive: 268, number of negative: 2487
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000184 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1929
[LightGBM] [Info] Number of data points in the train set: 2755, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.097278 -> initscore=-2.227845
[LightGBM] [Info] Start training from score -2.227845
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Test f1 score: 0.887218045112782
Outer train shape: (2755, 17)
test shape: (689, 17)
Inner train shape: (2204, 17)
val shape: (551, 17)
[LightGBM] [Warning] Categorical features with more bins than the configured maximum bin number found.
[LightGBM] [Warning] For categorical features, max_bin and max_bin_by_feature may be ignored with a large number of categories.
[LightGBM] [Info] Number of positive: 215, number of negative: 1989
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000302 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1833
[LightGBM] [Info] Number of data points in the train set: 2204, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.097550 -> initscore=-2.224749
[LightGBM] [Info] Start training from score -2.224749
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Early stopping, best iteration is:
[33]	valid_0's binary_logloss: 0.0410381
Best iteration: 33
Validation f1 score: 0.9320388349514563
Inner train shape: (2204, 17)
val shape: (551, 17)
[LightGBM] [Warning] Categorical features with more bins than the configured maximum bin number found.
[LightGBM] [Warning] For categorical features, max_bin and max_bin_by_feature may be ignored with a large number of categories.
[LightGBM] [Info] Number of positive: 215, number of negative: 1989
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000168 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1844
[LightGBM] [Info] Number of data points in the train set: 2204, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.097550 -> initscore=-2.224749
[LightGBM] [Info] Start training from score -2.224749
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Early stopping, best iteration is:
[38]	valid_0's binary_logloss: 0.0355839
Best iteration: 38
Validation f1 score: 0.9259259259259259
Inner train shape: (2204, 17)
val shape: (551, 17)
[LightGBM] [Warning] Categorical features with more bins than the configured maximum bin number found.
[LightGBM] [Warning] For categorical features, max_bin and max_bin_by_feature may be ignored with a large number of categories.
[LightGBM] [Info] Number of positive: 214, number of negative: 1990
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000316 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1842
[LightGBM] [Info] Number of data points in the train set: 2204, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.097096 -> initscore=-2.229914
[LightGBM] [Info] Start training from score -2.229914
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Early stopping, best iteration is:
[36]	valid_0's binary_logloss: 0.0434878
Best iteration: 36
Validation f1 score: 0.9056603773584906
Inner train shape: (2204, 17)
val shape: (551, 17)
[LightGBM] [Warning] Categorical features with more bins than the configured maximum bin number found.
[LightGBM] [Warning] For categorical features, max_bin and max_bin_by_feature may be ignored with a large number of categories.
[LightGBM] [Info] Number of positive: 214, number of negative: 1990
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000318 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1757
[LightGBM] [Info] Number of data points in the train set: 2204, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.097096 -> initscore=-2.229914
[LightGBM] [Info] Start training from score -2.229914
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Early stopping, best iteration is:
[42]	valid_0's binary_logloss: 0.0467974
Best iteration: 42
Validation f1 score: 0.9215686274509804
Inner train shape: (2204, 17)
val shape: (551, 17)
[LightGBM] [Warning] Categorical features with more bins than the configured maximum bin number found.
[LightGBM] [Warning] For categorical features, max_bin and max_bin_by_feature may be ignored with a large number of categories.
[LightGBM] [Info] Number of positive: 214, number of negative: 1990
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000155 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1828
[LightGBM] [Info] Number of data points in the train set: 2204, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.097096 -> initscore=-2.229914
[LightGBM] [Info] Start training from score -2.229914
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Early stopping, best iteration is:
[36]	valid_0's binary_logloss: 0.0446655
Best iteration: 36
Validation f1 score: 0.912621359223301
Average best iteration: 37.0
Scaled average best iteration: 46
[LightGBM] [Warning] Categorical features with more bins than the configured maximum bin number found.
[LightGBM] [Warning] For categorical features, max_bin and max_bin_by_feature may be ignored with a large number of categories.
[LightGBM] [Info] Number of positive: 268, number of negative: 2487
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000166 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1936
[LightGBM] [Info] Number of data points in the train set: 2755, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.097278 -> initscore=-2.227845
[LightGBM] [Info] Start training from score -2.227845
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Test f1 score: 0.8939393939393939
Outer train shape: (2755, 17)
test shape: (689, 17)
Inner train shape: (2204, 17)
val shape: (551, 17)
[LightGBM] [Warning] Categorical features with more bins than the configured maximum bin number found.
[LightGBM] [Warning] For categorical features, max_bin and max_bin_by_feature may be ignored with a large number of categories.
[LightGBM] [Info] Number of positive: 215, number of negative: 1989
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000321 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1840
[LightGBM] [Info] Number of data points in the train set: 2204, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.097550 -> initscore=-2.224749
[LightGBM] [Info] Start training from score -2.224749
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Early stopping, best iteration is:
[39]	valid_0's binary_logloss: 0.044607
Best iteration: 39
Validation f1 score: 0.923076923076923
Inner train shape: (2204, 17)
val shape: (551, 17)
[LightGBM] [Warning] Categorical features with more bins than the configured maximum bin number found.
[LightGBM] [Warning] For categorical features, max_bin and max_bin_by_feature may be ignored with a large number of categories.
[LightGBM] [Info] Number of positive: 215, number of negative: 1989
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000322 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1849
[LightGBM] [Info] Number of data points in the train set: 2204, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.097550 -> initscore=-2.224749
[LightGBM] [Info] Start training from score -2.224749
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Early stopping, best iteration is:
[54]	valid_0's binary_logloss: 0.0292189
Best iteration: 54
Validation f1 score: 0.9807692307692307
Inner train shape: (2204, 17)
val shape: (551, 17)
[LightGBM] [Warning] Categorical features with more bins than the configured maximum bin number found.
[LightGBM] [Warning] For categorical features, max_bin and max_bin_by_feature may be ignored with a large number of categories.
[LightGBM] [Info] Number of positive: 214, number of negative: 1990
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000306 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1850
[LightGBM] [Info] Number of data points in the train set: 2204, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.097096 -> initscore=-2.229914
[LightGBM] [Info] Start training from score -2.229914
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Early stopping, best iteration is:
[45]	valid_0's binary_logloss: 0.0408655
Best iteration: 45
Validation f1 score: 0.9215686274509804
Inner train shape: (2204, 17)
val shape: (551, 17)
[LightGBM] [Warning] Categorical features with more bins than the configured maximum bin number found.
[LightGBM] [Warning] For categorical features, max_bin and max_bin_by_feature may be ignored with a large number of categories.
[LightGBM] [Info] Number of positive: 214, number of negative: 1990
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000275 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1858
[LightGBM] [Info] Number of data points in the train set: 2204, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.097096 -> initscore=-2.229914
[LightGBM] [Info] Start training from score -2.229914
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Early stopping, best iteration is:
[36]	valid_0's binary_logloss: 0.0329233
Best iteration: 36
Validation f1 score: 0.9245283018867925
Inner train shape: (2204, 17)
val shape: (551, 17)
[LightGBM] [Warning] Categorical features with more bins than the configured maximum bin number found.
[LightGBM] [Warning] For categorical features, max_bin and max_bin_by_feature may be ignored with a large number of categories.
[LightGBM] [Info] Number of positive: 214, number of negative: 1990
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000294 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1838
[LightGBM] [Info] Number of data points in the train set: 2204, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.097096 -> initscore=-2.229914
[LightGBM] [Info] Start training from score -2.229914
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Early stopping, best iteration is:
[41]	valid_0's binary_logloss: 0.0555179
Best iteration: 41
Validation f1 score: 0.9009009009009009
Average best iteration: 43.0
Scaled average best iteration: 53
[LightGBM] [Warning] Categorical features with more bins than the configured maximum bin number found.
[LightGBM] [Warning] For categorical features, max_bin and max_bin_by_feature may be ignored with a large number of categories.
[LightGBM] [Info] Number of positive: 268, number of negative: 2487
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000681 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1945
[LightGBM] [Info] Number of data points in the train set: 2755, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.097278 -> initscore=-2.227845
[LightGBM] [Info] Start training from score -2.227845
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Test f1 score: 0.8854961832061069
Outer train shape: (2756, 17)
test shape: (688, 17)
Inner train shape: (2204, 17)
val shape: (552, 17)
[LightGBM] [Warning] Categorical features with more bins than the configured maximum bin number found.
[LightGBM] [Warning] For categorical features, max_bin and max_bin_by_feature may be ignored with a large number of categories.
[LightGBM] [Info] Number of positive: 214, number of negative: 1990
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000300 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1834
[LightGBM] [Info] Number of data points in the train set: 2204, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.097096 -> initscore=-2.229914
[LightGBM] [Info] Start training from score -2.229914
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Early stopping, best iteration is:
[42]	valid_0's binary_logloss: 0.0426305
Best iteration: 42
Validation f1 score: 0.9245283018867925
Inner train shape: (2205, 17)
val shape: (551, 17)
[LightGBM] [Warning] Categorical features with more bins than the configured maximum bin number found.
[LightGBM] [Warning] For categorical features, max_bin and max_bin_by_feature may be ignored with a large number of categories.
[LightGBM] [Info] Number of positive: 215, number of negative: 1990
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000312 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1834
[LightGBM] [Info] Number of data points in the train set: 2205, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.097506 -> initscore=-2.225252
[LightGBM] [Info] Start training from score -2.225252
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Early stopping, best iteration is:
[37]	valid_0's binary_logloss: 0.0327407
Best iteration: 37
Validation f1 score: 0.9215686274509803
Inner train shape: (2205, 17)
val shape: (551, 17)
[LightGBM] [Warning] Categorical features with more bins than the configured maximum bin number found.
[LightGBM] [Warning] For categorical features, max_bin and max_bin_by_feature may be ignored with a large number of categories.
[LightGBM] [Info] Number of positive: 215, number of negative: 1990
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000378 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1835
[LightGBM] [Info] Number of data points in the train set: 2205, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.097506 -> initscore=-2.225252
[LightGBM] [Info] Start training from score -2.225252
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Early stopping, best iteration is:
[37]	valid_0's binary_logloss: 0.0506723
Best iteration: 37
Validation f1 score: 0.897196261682243
Inner train shape: (2205, 17)
val shape: (551, 17)
[LightGBM] [Warning] Categorical features with more bins than the configured maximum bin number found.
[LightGBM] [Warning] For categorical features, max_bin and max_bin_by_feature may be ignored with a large number of categories.
[LightGBM] [Info] Number of positive: 214, number of negative: 1991
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000145 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1822
[LightGBM] [Info] Number of data points in the train set: 2205, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.097052 -> initscore=-2.230416
[LightGBM] [Info] Start training from score -2.230416
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Early stopping, best iteration is:
[28]	valid_0's binary_logloss: 0.0563715
Best iteration: 28
Validation f1 score: 0.8867924528301887
Inner train shape: (2205, 17)
val shape: (551, 17)
[LightGBM] [Warning] Categorical features with more bins than the configured maximum bin number found.
[LightGBM] [Warning] For categorical features, max_bin and max_bin_by_feature may be ignored with a large number of categories.
[LightGBM] [Info] Number of positive: 214, number of negative: 1991
[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000307 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1836
[LightGBM] [Info] Number of data points in the train set: 2205, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.097052 -> initscore=-2.230416
[LightGBM] [Info] Start training from score -2.230416
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Training until validation scores don't improve for 5 rounds
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Early stopping, best iteration is:
[36]	valid_0's binary_logloss: 0.04185
Best iteration: 36
Validation f1 score: 0.9357798165137615
Average best iteration: 36.0
Scaled average best iteration: 45
[LightGBM] [Warning] Categorical features with more bins than the configured maximum bin number found.
[LightGBM] [Warning] For categorical features, max_bin and max_bin_by_feature may be ignored with a large number of categories.
[LightGBM] [Info] Number of positive: 268, number of negative: 2488
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000231 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1931
[LightGBM] [Info] Number of data points in the train set: 2756, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.097242 -> initscore=-2.228247
[LightGBM] [Info] Start training from score -2.228247
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
Test f1 score: 0.9172932330827067

Compute average LightGBM performance on validation and test sets

avg_val_f1 = np.mean(stratified_kfold_results['val_f1'])
avg_test_f1 = np.mean(stratified_kfold_results['test_f1'])
avg_num_boost_rounds = int(np.mean(stratified_kfold_results['test_num_boost_rounds']))

print(f'Average validation f1: {avg_val_f1}')
print(f'Average test f1: {avg_test_f1}')
print(f'Average test num_boost_rounds: {avg_num_boost_rounds}')

Average validation f1: 0.9158992119013841
Average test f1: 0.9034560377348646
Average test num_boost_rounds: 47

Train LightGBM on full train dataset

model_path = './models/lgb_model_final.txt'
full_num_boost_rounds = int(np.mean(stratified_kfold_results['test_num_boost_rounds']) * scaling_factor)
print(f' Number of iterations used to train LightGBM model on full dataset: {full_num_boost_rounds}')
lgb_model = train_lgb_model(
    train = train, 
    num_boost_rounds = full_num_boost_rounds, 
    features = features, 
    cat_features = cat_features,
    model_path = model_path,
)

 Number of iterations used to train LightGBM model on full dataset: 59
[LightGBM] [Warning] Categorical features with more bins than the configured maximum bin number found.
[LightGBM] [Warning] For categorical features, max_bin and max_bin_by_feature may be ignored with a large number of categories.
[LightGBM] [Info] Number of positive: 335, number of negative: 3109
[LightGBM] [Warning] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000235 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 1991
[LightGBM] [Info] Number of data points in the train set: 3444, number of used features: 15
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.097271 -> initscore=-2.227926
[LightGBM] [Info] Start training from score -2.227926
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf

Compute LightGBM performance on actual held out test dataset

test_preds = (lgb_model.predict(test[features]) > threshold).astype(int)
lgb_performance_dict = classification_report(test[label_col], test_preds, output_dict = True)['1']
lgb_performance_dict['model'] = model_name

print(classification_report(test[label_col], test_preds))

              precision    recall  f1-score   support

           0       0.98      0.99      0.99      1332
           1       0.94      0.83      0.88       144

    accuracy                           0.98      1476
   macro avg       0.96      0.91      0.93      1476
weighted avg       0.98      0.98      0.98      1476

Perform Nested Stratified K-Fold Cross-Validation for CatBoost

model_name = 'catboost'
stratified_kfold_results = nested_stratified_kfold_cv(train, features, cat_features, model_name, num_folds = num_folds, scaling_factor = scaling_factor)

Outer train shape: (2755, 17)
test shape: (689, 17)
Inner train shape: (2204, 17)
val shape: (551, 17)
Learning rate set to 0.104877
0:	learn: 0.6137099	test: 0.6148079	best: 0.6148079 (0)	total: 93.1ms	remaining: 9.21s
1:	learn: 0.5491618	test: 0.5512964	best: 0.5512964 (1)	total: 95.5ms	remaining: 4.68s
2:	learn: 0.4927994	test: 0.4953503	best: 0.4953503 (2)	total: 97.3ms	remaining: 3.15s
3:	learn: 0.4447187	test: 0.4467030	best: 0.4467030 (3)	total: 98.5ms	remaining: 2.36s
4:	learn: 0.4070794	test: 0.4087971	best: 0.4087971 (4)	total: 100ms	remaining: 1.91s
5:	learn: 0.3737032	test: 0.3759344	best: 0.3759344 (5)	total: 104ms	remaining: 1.62s
6:	learn: 0.3407056	test: 0.3448025	best: 0.3448025 (6)	total: 106ms	remaining: 1.41s
7:	learn: 0.3120391	test: 0.3189019	best: 0.3189019 (7)	total: 108ms	remaining: 1.24s
8:	learn: 0.2884489	test: 0.2959632	best: 0.2959632 (8)	total: 109ms	remaining: 1.1s
9:	learn: 0.2677752	test: 0.2749242	best: 0.2749242 (9)	total: 111ms	remaining: 996ms
10:	learn: 0.2492560	test: 0.2565728	best: 0.2565728 (10)	total: 112ms	remaining: 909ms
11:	learn: 0.2308614	test: 0.2408341	best: 0.2408341 (11)	total: 114ms	remaining: 837ms
12:	learn: 0.2159768	test: 0.2257818	best: 0.2257818 (12)	total: 116ms	remaining: 777ms
13:	learn: 0.2028503	test: 0.2127756	best: 0.2127756 (13)	total: 118ms	remaining: 724ms
14:	learn: 0.1900837	test: 0.2012779	best: 0.2012779 (14)	total: 120ms	remaining: 679ms
15:	learn: 0.1802583	test: 0.1910384	best: 0.1910384 (15)	total: 122ms	remaining: 641ms
16:	learn: 0.1719172	test: 0.1839119	best: 0.1839119 (16)	total: 124ms	remaining: 605ms
17:	learn: 0.1609053	test: 0.1734636	best: 0.1734636 (17)	total: 129ms	remaining: 586ms
18:	learn: 0.1527500	test: 0.1653827	best: 0.1653827 (18)	total: 130ms	remaining: 556ms
19:	learn: 0.1432336	test: 0.1574614	best: 0.1574614 (19)	total: 133ms	remaining: 531ms
20:	learn: 0.1336638	test: 0.1484680	best: 0.1484680 (20)	total: 135ms	remaining: 508ms
21:	learn: 0.1274356	test: 0.1423106	best: 0.1423106 (21)	total: 137ms	remaining: 486ms
22:	learn: 0.1196924	test: 0.1352819	best: 0.1352819 (22)	total: 139ms	remaining: 466ms
23:	learn: 0.1122721	test: 0.1289173	best: 0.1289173 (23)	total: 141ms	remaining: 447ms
24:	learn: 0.1064186	test: 0.1237954	best: 0.1237954 (24)	total: 143ms	remaining: 429ms
25:	learn: 0.1047254	test: 0.1226685	best: 0.1226685 (25)	total: 144ms	remaining: 409ms
26:	learn: 0.0986352	test: 0.1173750	best: 0.1173750 (26)	total: 145ms	remaining: 393ms
27:	learn: 0.0933945	test: 0.1128597	best: 0.1128597 (27)	total: 149ms	remaining: 382ms
28:	learn: 0.0887185	test: 0.1089968	best: 0.1089968 (28)	total: 152ms	remaining: 372ms
29:	learn: 0.0857051	test: 0.1061398	best: 0.1061398 (29)	total: 154ms	remaining: 360ms
30:	learn: 0.0818617	test: 0.1021175	best: 0.1021175 (30)	total: 156ms	remaining: 347ms
31:	learn: 0.0797039	test: 0.1010326	best: 0.1010326 (31)	total: 159ms	remaining: 338ms
32:	learn: 0.0762767	test: 0.0980065	best: 0.0980065 (32)	total: 161ms	remaining: 326ms
33:	learn: 0.0728253	test: 0.0952108	best: 0.0952108 (33)	total: 162ms	remaining: 315ms
34:	learn: 0.0704511	test: 0.0935729	best: 0.0935729 (34)	total: 164ms	remaining: 305ms
35:	learn: 0.0677836	test: 0.0913673	best: 0.0913673 (35)	total: 167ms	remaining: 298ms
36:	learn: 0.0649232	test: 0.0894406	best: 0.0894406 (36)	total: 170ms	remaining: 289ms
37:	learn: 0.0630081	test: 0.0875116	best: 0.0875116 (37)	total: 171ms	remaining: 280ms
38:	learn: 0.0606744	test: 0.0855693	best: 0.0855693 (38)	total: 173ms	remaining: 271ms
39:	learn: 0.0583917	test: 0.0839389	best: 0.0839389 (39)	total: 175ms	remaining: 263ms
40:	learn: 0.0572759	test: 0.0833833	best: 0.0833833 (40)	total: 177ms	remaining: 255ms
41:	learn: 0.0563682	test: 0.0833414	best: 0.0833414 (41)	total: 179ms	remaining: 247ms
42:	learn: 0.0551411	test: 0.0827534	best: 0.0827534 (42)	total: 181ms	remaining: 240ms
43:	learn: 0.0535011	test: 0.0814597	best: 0.0814597 (43)	total: 183ms	remaining: 233ms
44:	learn: 0.0523051	test: 0.0802426	best: 0.0802426 (44)	total: 186ms	remaining: 227ms
45:	learn: 0.0510218	test: 0.0790557	best: 0.0790557 (45)	total: 187ms	remaining: 220ms
46:	learn: 0.0502966	test: 0.0788869	best: 0.0788869 (46)	total: 189ms	remaining: 213ms
47:	learn: 0.0489385	test: 0.0776926	best: 0.0776926 (47)	total: 191ms	remaining: 207ms
48:	learn: 0.0478509	test: 0.0761281	best: 0.0761281 (48)	total: 193ms	remaining: 201ms
49:	learn: 0.0470305	test: 0.0755636	best: 0.0755636 (49)	total: 195ms	remaining: 195ms
50:	learn: 0.0458221	test: 0.0752454	best: 0.0752454 (50)	total: 199ms	remaining: 191ms
51:	learn: 0.0448845	test: 0.0746095	best: 0.0746095 (51)	total: 202ms	remaining: 187ms
52:	learn: 0.0443872	test: 0.0742595	best: 0.0742595 (52)	total: 204ms	remaining: 181ms
53:	learn: 0.0436146	test: 0.0736916	best: 0.0736916 (53)	total: 206ms	remaining: 176ms
54:	learn: 0.0430764	test: 0.0733191	best: 0.0733191 (54)	total: 208ms	remaining: 170ms
55:	learn: 0.0423815	test: 0.0725687	best: 0.0725687 (55)	total: 211ms	remaining: 166ms
56:	learn: 0.0418934	test: 0.0723442	best: 0.0723442 (56)	total: 215ms	remaining: 162ms
57:	learn: 0.0411001	test: 0.0715643	best: 0.0715643 (57)	total: 218ms	remaining: 158ms
58:	learn: 0.0403001	test: 0.0710690	best: 0.0710690 (58)	total: 220ms	remaining: 153ms
59:	learn: 0.0400637	test: 0.0710001	best: 0.0710001 (59)	total: 222ms	remaining: 148ms
60:	learn: 0.0392505	test: 0.0708628	best: 0.0708628 (60)	total: 224ms	remaining: 143ms
61:	learn: 0.0387767	test: 0.0701717	best: 0.0701717 (61)	total: 226ms	remaining: 138ms
62:	learn: 0.0382934	test: 0.0700759	best: 0.0700759 (62)	total: 227ms	remaining: 134ms
63:	learn: 0.0379435	test: 0.0696984	best: 0.0696984 (63)	total: 229ms	remaining: 129ms
64:	learn: 0.0374735	test: 0.0692895	best: 0.0692895 (64)	total: 231ms	remaining: 125ms
65:	learn: 0.0370069	test: 0.0691191	best: 0.0691191 (65)	total: 233ms	remaining: 120ms
66:	learn: 0.0366235	test: 0.0688495	best: 0.0688495 (66)	total: 235ms	remaining: 116ms
67:	learn: 0.0363540	test: 0.0686624	best: 0.0686624 (67)	total: 237ms	remaining: 111ms
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99:	learn: 0.0275201	test: 0.0648202	best: 0.0648202 (99)	total: 326ms	remaining: 0us

bestTest = 0.06482016316
bestIteration = 99

Best iteration: 99
Validation f1 score: 0.8484848484848485
Inner train shape: (2204, 17)
val shape: (551, 17)
Learning rate set to 0.104877
0:	learn: 0.6118362	test: 0.6094980	best: 0.6094980 (0)	total: 1.29ms	remaining: 128ms
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99:	learn: 0.0286764	test: 0.0343356	best: 0.0343356 (99)	total: 197ms	remaining: 0us

bestTest = 0.03433557859
bestIteration = 99

Best iteration: 99
Validation f1 score: 0.9514563106796116
Inner train shape: (2204, 17)
val shape: (551, 17)
Learning rate set to 0.104877
0:	learn: 0.6148215	test: 0.6140571	best: 0.6140571 (0)	total: 2.09ms	remaining: 207ms
1:	learn: 0.5470796	test: 0.5472121	best: 0.5472121 (1)	total: 3.96ms	remaining: 194ms
2:	learn: 0.4948127	test: 0.4959976	best: 0.4959976 (2)	total: 5.77ms	remaining: 187ms
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6:	learn: 0.3314980	test: 0.3335197	best: 0.3335197 (6)	total: 11.6ms	remaining: 154ms
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99:	learn: 0.0268519	test: 0.0404661	best: 0.0404661 (99)	total: 318ms	remaining: 0us

bestTest = 0.04046608713
bestIteration = 99

Best iteration: 99
Validation f1 score: 0.923076923076923
Inner train shape: (2204, 17)
val shape: (551, 17)
Learning rate set to 0.104877
0:	learn: 0.6135400	test: 0.6144742	best: 0.6144742 (0)	total: 1.99ms	remaining: 197ms
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99:	learn: 0.0263700	test: 0.0576712	best: 0.0576712 (99)	total: 190ms	remaining: 0us

bestTest = 0.05767122401
bestIteration = 99

Best iteration: 99
Validation f1 score: 0.8800000000000001
Inner train shape: (2204, 17)
val shape: (551, 17)
Learning rate set to 0.104877
0:	learn: 0.6111058	test: 0.6103320	best: 0.6103320 (0)	total: 907us	remaining: 89.9ms
1:	learn: 0.5476521	test: 0.5430159	best: 0.5430159 (1)	total: 2.77ms	remaining: 136ms
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99:	learn: 0.0300049	test: 0.0300238	best: 0.0300238 (99)	total: 191ms	remaining: 0us

bestTest = 0.03002375978
bestIteration = 99

Best iteration: 99
Validation f1 score: 0.9615384615384615
Average best iteration: 99.0
Scaled average best iteration: 123
Learning rate set to 0.108498
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121:	learn: 0.0235321	total: 218ms	remaining: 1.78ms
122:	learn: 0.0233154	total: 219ms	remaining: 0us
Test f1 score: 0.9473684210526316
Outer train shape: (2755, 17)
test shape: (689, 17)
Inner train shape: (2204, 17)
val shape: (551, 17)
Learning rate set to 0.104877
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99:	learn: 0.0267194	test: 0.0352558	best: 0.0352558 (99)	total: 164ms	remaining: 0us

bestTest = 0.03525582624
bestIteration = 99

Best iteration: 99
Validation f1 score: 0.9504950495049505
Inner train shape: (2204, 17)
val shape: (551, 17)
Learning rate set to 0.104877
0:	learn: 0.6095212	test: 0.6098616	best: 0.6098616 (0)	total: 954us	remaining: 94.5ms
1:	learn: 0.5423442	test: 0.5411111	best: 0.5411111 (1)	total: 2.32ms	remaining: 114ms
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6:	learn: 0.3301615	test: 0.3290814	best: 0.3290814 (6)	total: 8.79ms	remaining: 117ms
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90:	learn: 0.0310299	test: 0.0419200	best: 0.0419200 (90)	total: 128ms	remaining: 12.7ms
91:	learn: 0.0308518	test: 0.0418375	best: 0.0418375 (91)	total: 129ms	remaining: 11.2ms
92:	learn: 0.0305583	test: 0.0415488	best: 0.0415488 (92)	total: 131ms	remaining: 9.84ms
93:	learn: 0.0303714	test: 0.0413178	best: 0.0413178 (93)	total: 132ms	remaining: 8.43ms
94:	learn: 0.0301578	test: 0.0411125	best: 0.0411125 (94)	total: 134ms	remaining: 7.03ms
95:	learn: 0.0297861	test: 0.0409239	best: 0.0409239 (95)	total: 135ms	remaining: 5.62ms
96:	learn: 0.0294366	test: 0.0407010	best: 0.0407010 (96)	total: 136ms	remaining: 4.21ms
97:	learn: 0.0291745	test: 0.0404396	best: 0.0404396 (97)	total: 138ms	remaining: 2.81ms
98:	learn: 0.0290200	test: 0.0403588	best: 0.0403588 (98)	total: 139ms	remaining: 1.41ms
99:	learn: 0.0287211	test: 0.0399988	best: 0.0399988 (99)	total: 141ms	remaining: 0us

bestTest = 0.03999877494
bestIteration = 99

Best iteration: 99
Validation f1 score: 0.9306930693069307
Inner train shape: (2204, 17)
val shape: (551, 17)
Learning rate set to 0.104877
0:	learn: 0.6091332	test: 0.6101079	best: 0.6101079 (0)	total: 881us	remaining: 87.3ms
1:	learn: 0.5410836	test: 0.5429300	best: 0.5429300 (1)	total: 2.88ms	remaining: 141ms
2:	learn: 0.4833413	test: 0.4860142	best: 0.4860142 (2)	total: 4.38ms	remaining: 142ms
3:	learn: 0.4372878	test: 0.4405915	best: 0.4405915 (3)	total: 5.97ms	remaining: 143ms
4:	learn: 0.3919925	test: 0.3966549	best: 0.3966549 (4)	total: 7.01ms	remaining: 133ms
5:	learn: 0.3542237	test: 0.3598058	best: 0.3598058 (5)	total: 8.3ms	remaining: 130ms
6:	learn: 0.3237832	test: 0.3304364	best: 0.3304364 (6)	total: 9.64ms	remaining: 128ms
7:	learn: 0.2964122	test: 0.3040630	best: 0.3040630 (7)	total: 10.9ms	remaining: 126ms
8:	learn: 0.2705386	test: 0.2784006	best: 0.2784006 (8)	total: 12.4ms	remaining: 126ms
9:	learn: 0.2493939	test: 0.2582883	best: 0.2582883 (9)	total: 13.7ms	remaining: 124ms
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18:	learn: 0.1280580	test: 0.1433038	best: 0.1433038 (18)	total: 27.4ms	remaining: 117ms
19:	learn: 0.1215453	test: 0.1372183	best: 0.1372183 (19)	total: 28.9ms	remaining: 116ms
20:	learn: 0.1142045	test: 0.1309314	best: 0.1309314 (20)	total: 30.5ms	remaining: 115ms
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99:	learn: 0.0233296	test: 0.0576358	best: 0.0576358 (99)	total: 150ms	remaining: 0us

bestTest = 0.05763577992
bestIteration = 99

Best iteration: 99
Validation f1 score: 0.8627450980392156
Inner train shape: (2204, 17)
val shape: (551, 17)
Learning rate set to 0.104877
0:	learn: 0.6132796	test: 0.6123633	best: 0.6123633 (0)	total: 1.91ms	remaining: 189ms
1:	learn: 0.5465065	test: 0.5448799	best: 0.5448799 (1)	total: 3.35ms	remaining: 164ms
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95:	learn: 0.0269838	test: 0.0406480	best: 0.0406480 (95)	total: 163ms	remaining: 6.8ms
96:	learn: 0.0268644	test: 0.0404976	best: 0.0404976 (96)	total: 165ms	remaining: 5.09ms
97:	learn: 0.0268367	test: 0.0404598	best: 0.0404598 (97)	total: 166ms	remaining: 3.39ms
98:	learn: 0.0264755	test: 0.0402342	best: 0.0402342 (98)	total: 168ms	remaining: 1.69ms
99:	learn: 0.0263648	test: 0.0400923	best: 0.0400923 (99)	total: 169ms	remaining: 0us

bestTest = 0.04009230377
bestIteration = 99

Best iteration: 99
Validation f1 score: 0.9423076923076924
Inner train shape: (2204, 17)
val shape: (551, 17)
Learning rate set to 0.104877
0:	learn: 0.6101510	test: 0.6082137	best: 0.6082137 (0)	total: 848us	remaining: 84ms
1:	learn: 0.5469908	test: 0.5443914	best: 0.5443914 (1)	total: 2.31ms	remaining: 113ms
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96:	learn: 0.0257583	test: 0.0386167	best: 0.0386167 (96)	total: 140ms	remaining: 4.32ms
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98:	learn: 0.0256145	test: 0.0384643	best: 0.0384643 (98)	total: 142ms	remaining: 1.43ms
99:	learn: 0.0255909	test: 0.0384615	best: 0.0384615 (99)	total: 143ms	remaining: 0us

bestTest = 0.03846151798
bestIteration = 99

Best iteration: 99
Validation f1 score: 0.9411764705882353
Average best iteration: 99.0
Scaled average best iteration: 123
Learning rate set to 0.108498
0:	learn: 0.6101332	total: 1.85ms	remaining: 226ms
1:	learn: 0.5416074	total: 3.38ms	remaining: 205ms
2:	learn: 0.4800055	total: 4.91ms	remaining: 196ms
3:	learn: 0.4275741	total: 6.21ms	remaining: 185ms
4:	learn: 0.3861063	total: 7.54ms	remaining: 178ms
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105:	learn: 0.0255647	total: 149ms	remaining: 23.8ms
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107:	learn: 0.0249551	total: 151ms	remaining: 21ms
108:	learn: 0.0249540	total: 153ms	remaining: 19.6ms
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110:	learn: 0.0244688	total: 156ms	remaining: 16.8ms
111:	learn: 0.0243334	total: 157ms	remaining: 15.4ms
112:	learn: 0.0242278	total: 159ms	remaining: 14ms
113:	learn: 0.0242269	total: 160ms	remaining: 12.6ms
114:	learn: 0.0240550	total: 162ms	remaining: 11.2ms
115:	learn: 0.0240545	total: 163ms	remaining: 9.83ms
116:	learn: 0.0240540	total: 164ms	remaining: 8.42ms
117:	learn: 0.0240536	total: 166ms	remaining: 7.02ms
118:	learn: 0.0239611	total: 167ms	remaining: 5.62ms
119:	learn: 0.0238649	total: 169ms	remaining: 4.21ms
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121:	learn: 0.0236257	total: 171ms	remaining: 1.4ms
122:	learn: 0.0233856	total: 173ms	remaining: 0us
Test f1 score: 0.9133858267716535
Outer train shape: (2755, 17)
test shape: (689, 17)
Inner train shape: (2204, 17)
val shape: (551, 17)
Learning rate set to 0.104877
0:	learn: 0.6142807	test: 0.6113546	best: 0.6113546 (0)	total: 1.99ms	remaining: 197ms
1:	learn: 0.5479658	test: 0.5438182	best: 0.5438182 (1)	total: 3.65ms	remaining: 179ms
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bestTest = 0.04656710926
bestIteration = 98

Shrink model to first 99 iterations.
Best iteration: 98
Validation f1 score: 0.9199999999999999
Inner train shape: (2204, 17)
val shape: (551, 17)
Learning rate set to 0.104877
0:	learn: 0.6109688	test: 0.6089537	best: 0.6089537 (0)	total: 1.25ms	remaining: 124ms
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99:	learn: 0.0286566	test: 0.0348866	best: 0.0348866 (99)	total: 139ms	remaining: 0us

bestTest = 0.03488663709
bestIteration = 99

Best iteration: 99
Validation f1 score: 0.9423076923076923
Inner train shape: (2204, 17)
val shape: (551, 17)
Learning rate set to 0.104877
0:	learn: 0.6137593	test: 0.6142508	best: 0.6142508 (0)	total: 6.72ms	remaining: 665ms
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99:	learn: 0.0303303	test: 0.0402870	best: 0.0402870 (99)	total: 209ms	remaining: 0us

bestTest = 0.04028704391
bestIteration = 99

Best iteration: 99
Validation f1 score: 0.912621359223301
Inner train shape: (2204, 17)
val shape: (551, 17)
Learning rate set to 0.104877
0:	learn: 0.6091091	test: 0.6121897	best: 0.6121897 (0)	total: 1.87ms	remaining: 185ms
1:	learn: 0.5391781	test: 0.5429175	best: 0.5429175 (1)	total: 4.75ms	remaining: 233ms
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99:	learn: 0.0260808	test: 0.0491528	best: 0.0491528 (99)	total: 204ms	remaining: 0us

bestTest = 0.04915275698
bestIteration = 99

Best iteration: 99
Validation f1 score: 0.9108910891089108
Inner train shape: (2204, 17)
val shape: (551, 17)
Learning rate set to 0.104877
0:	learn: 0.6100154	test: 0.6099688	best: 0.6099688 (0)	total: 868us	remaining: 86ms
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99:	learn: 0.0282898	test: 0.0397204	best: 0.0397204 (99)	total: 135ms	remaining: 0us

bestTest = 0.03972041059
bestIteration = 99

Best iteration: 99
Validation f1 score: 0.9108910891089108
Average best iteration: 98.8
Scaled average best iteration: 123
Learning rate set to 0.108498
0:	learn: 0.6056623	total: 2.09ms	remaining: 255ms
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114:	learn: 0.0239693	total: 163ms	remaining: 11.3ms
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116:	learn: 0.0236118	total: 167ms	remaining: 8.59ms
117:	learn: 0.0234726	total: 169ms	remaining: 7.16ms
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121:	learn: 0.0229661	total: 175ms	remaining: 1.43ms
122:	learn: 0.0229330	total: 176ms	remaining: 0us
Test f1 score: 0.9291338582677166
Outer train shape: (2755, 17)
test shape: (689, 17)
Inner train shape: (2204, 17)
val shape: (551, 17)
Learning rate set to 0.104877
0:	learn: 0.6123946	test: 0.6144078	best: 0.6144078 (0)	total: 1.99ms	remaining: 197ms
1:	learn: 0.5440187	test: 0.5449018	best: 0.5449018 (1)	total: 3.81ms	remaining: 186ms
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9:	learn: 0.2561501	test: 0.2592507	best: 0.2592507 (9)	total: 14.3ms	remaining: 128ms
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90:	learn: 0.0272790	test: 0.0463903	best: 0.0463903 (90)	total: 132ms	remaining: 13.1ms
91:	learn: 0.0270608	test: 0.0462770	best: 0.0462770 (91)	total: 134ms	remaining: 11.6ms
92:	learn: 0.0268640	test: 0.0462772	best: 0.0462770 (91)	total: 135ms	remaining: 10.2ms
93:	learn: 0.0265920	test: 0.0462899	best: 0.0462770 (91)	total: 136ms	remaining: 8.7ms
94:	learn: 0.0263862	test: 0.0461346	best: 0.0461346 (94)	total: 138ms	remaining: 7.25ms
95:	learn: 0.0260765	test: 0.0458430	best: 0.0458430 (95)	total: 139ms	remaining: 5.8ms
96:	learn: 0.0257926	test: 0.0456980	best: 0.0456980 (96)	total: 141ms	remaining: 4.35ms
97:	learn: 0.0255861	test: 0.0457628	best: 0.0456980 (96)	total: 142ms	remaining: 2.9ms
98:	learn: 0.0254291	test: 0.0457532	best: 0.0456980 (96)	total: 143ms	remaining: 1.45ms
99:	learn: 0.0252274	test: 0.0456457	best: 0.0456457 (99)	total: 145ms	remaining: 0us

bestTest = 0.0456457176
bestIteration = 99

Best iteration: 99
Validation f1 score: 0.9306930693069307
Inner train shape: (2204, 17)
val shape: (551, 17)
Learning rate set to 0.104877
0:	learn: 0.6098312	test: 0.6093819	best: 0.6093819 (0)	total: 862us	remaining: 85.3ms
1:	learn: 0.5414307	test: 0.5413470	best: 0.5413470 (1)	total: 2.46ms	remaining: 120ms
2:	learn: 0.4862842	test: 0.4861102	best: 0.4861102 (2)	total: 3.94ms	remaining: 127ms
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4:	learn: 0.3948533	test: 0.3953340	best: 0.3953340 (4)	total: 6.53ms	remaining: 124ms
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99:	learn: 0.0269894	test: 0.0385419	best: 0.0385419 (99)	total: 138ms	remaining: 0us

bestTest = 0.03854193517
bestIteration = 99

Best iteration: 99
Validation f1 score: 0.9292929292929293
Inner train shape: (2204, 17)
val shape: (551, 17)
Learning rate set to 0.104877
0:	learn: 0.6096111	test: 0.6099461	best: 0.6099461 (0)	total: 855us	remaining: 84.6ms
1:	learn: 0.5441511	test: 0.5430135	best: 0.5430135 (1)	total: 2.55ms	remaining: 125ms
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5:	learn: 0.3664380	test: 0.3678825	best: 0.3678825 (5)	total: 8.17ms	remaining: 128ms
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90:	learn: 0.0288394	test: 0.0405654	best: 0.0405654 (90)	total: 128ms	remaining: 12.7ms
91:	learn: 0.0286464	test: 0.0404189	best: 0.0404189 (91)	total: 130ms	remaining: 11.3ms
92:	learn: 0.0283921	test: 0.0402516	best: 0.0402516 (92)	total: 131ms	remaining: 9.87ms
93:	learn: 0.0281980	test: 0.0401235	best: 0.0401235 (93)	total: 133ms	remaining: 8.46ms
94:	learn: 0.0280448	test: 0.0399802	best: 0.0399802 (94)	total: 134ms	remaining: 7.05ms
95:	learn: 0.0278594	test: 0.0397393	best: 0.0397393 (95)	total: 135ms	remaining: 5.63ms
96:	learn: 0.0276453	test: 0.0396811	best: 0.0396811 (96)	total: 137ms	remaining: 4.23ms
97:	learn: 0.0275008	test: 0.0396124	best: 0.0396124 (97)	total: 138ms	remaining: 2.82ms
98:	learn: 0.0273066	test: 0.0395751	best: 0.0395751 (98)	total: 140ms	remaining: 1.41ms
99:	learn: 0.0271898	test: 0.0394144	best: 0.0394144 (99)	total: 141ms	remaining: 0us

bestTest = 0.03941440313
bestIteration = 99

Best iteration: 99
Validation f1 score: 0.92
Inner train shape: (2204, 17)
val shape: (551, 17)
Learning rate set to 0.104877
0:	learn: 0.6100898	test: 0.6081642	best: 0.6081642 (0)	total: 998us	remaining: 98.9ms
1:	learn: 0.5440325	test: 0.5413015	best: 0.5413015 (1)	total: 2.66ms	remaining: 130ms
2:	learn: 0.4920906	test: 0.4876191	best: 0.4876191 (2)	total: 3.96ms	remaining: 128ms
3:	learn: 0.4453316	test: 0.4396276	best: 0.4396276 (3)	total: 5.26ms	remaining: 126ms
4:	learn: 0.4027042	test: 0.3954320	best: 0.3954320 (4)	total: 6.25ms	remaining: 119ms
5:	learn: 0.3683552	test: 0.3602773	best: 0.3602773 (5)	total: 7.52ms	remaining: 118ms
6:	learn: 0.3379110	test: 0.3293466	best: 0.3293466 (6)	total: 8.87ms	remaining: 118ms
7:	learn: 0.3110688	test: 0.3035576	best: 0.3035576 (7)	total: 10.2ms	remaining: 117ms
8:	learn: 0.2864347	test: 0.2785320	best: 0.2785320 (8)	total: 11.5ms	remaining: 116ms
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19:	learn: 0.1382595	test: 0.1279939	best: 0.1279939 (19)	total: 25.3ms	remaining: 101ms
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21:	learn: 0.1234064	test: 0.1132672	best: 0.1132672 (21)	total: 28ms	remaining: 99.4ms
22:	learn: 0.1168046	test: 0.1070607	best: 0.1070607 (22)	total: 29.4ms	remaining: 98.3ms
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25:	learn: 0.1018174	test: 0.0921689	best: 0.0921689 (25)	total: 33.6ms	remaining: 95.6ms
26:	learn: 0.0975474	test: 0.0878301	best: 0.0878301 (26)	total: 35ms	remaining: 94.5ms
27:	learn: 0.0935805	test: 0.0840588	best: 0.0840588 (27)	total: 36.4ms	remaining: 93.7ms
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30:	learn: 0.0829871	test: 0.0733090	best: 0.0733090 (30)	total: 40.8ms	remaining: 90.8ms
31:	learn: 0.0792565	test: 0.0698125	best: 0.0698125 (31)	total: 42.2ms	remaining: 89.7ms
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99:	learn: 0.0306011	test: 0.0299569	best: 0.0299569 (99)	total: 147ms	remaining: 0us

bestTest = 0.02995688422
bestIteration = 99

Best iteration: 99
Validation f1 score: 0.9523809523809523
Inner train shape: (2204, 17)
val shape: (551, 17)
Learning rate set to 0.104877
0:	learn: 0.6096856	test: 0.6095278	best: 0.6095278 (0)	total: 1.59ms	remaining: 158ms
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96:	learn: 0.0257072	test: 0.0559725	best: 0.0559725 (96)	total: 139ms	remaining: 4.29ms
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98:	learn: 0.0252325	test: 0.0556204	best: 0.0556204 (98)	total: 142ms	remaining: 1.43ms
99:	learn: 0.0249843	test: 0.0554633	best: 0.0554633 (99)	total: 143ms	remaining: 0us

bestTest = 0.0554633235
bestIteration = 99

Best iteration: 99
Validation f1 score: 0.8952380952380952
Average best iteration: 99.0
Scaled average best iteration: 123
Learning rate set to 0.108498
0:	learn: 0.6084080	total: 2.11ms	remaining: 257ms
1:	learn: 0.5371136	total: 3.88ms	remaining: 235ms
2:	learn: 0.4749946	total: 5.6ms	remaining: 224ms
3:	learn: 0.4244265	total: 6.72ms	remaining: 200ms
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122:	learn: 0.0239860	total: 229ms	remaining: 0us
Test f1 score: 0.9291338582677166
Outer train shape: (2756, 17)
test shape: (688, 17)
Inner train shape: (2204, 17)
val shape: (552, 17)
Learning rate set to 0.104877
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95:	learn: 0.0269506	test: 0.0468422	best: 0.0468422 (95)	total: 218ms	remaining: 9.08ms
96:	learn: 0.0268076	test: 0.0467259	best: 0.0467259 (96)	total: 220ms	remaining: 6.79ms
97:	learn: 0.0266543	test: 0.0465942	best: 0.0465942 (97)	total: 221ms	remaining: 4.51ms
98:	learn: 0.0265243	test: 0.0466014	best: 0.0465942 (97)	total: 223ms	remaining: 2.25ms
99:	learn: 0.0264157	test: 0.0465321	best: 0.0465321 (99)	total: 224ms	remaining: 0us

bestTest = 0.04653206438
bestIteration = 99

Best iteration: 99
Validation f1 score: 0.9019607843137256
Inner train shape: (2205, 17)
val shape: (551, 17)
Learning rate set to 0.104889
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99:	learn: 0.0288080	test: 0.0335230	best: 0.0335230 (99)	total: 211ms	remaining: 0us

bestTest = 0.03352298549
bestIteration = 99

Best iteration: 99
Validation f1 score: 0.9504950495049505
Inner train shape: (2205, 17)
val shape: (551, 17)
Learning rate set to 0.104889
0:	learn: 0.6105211	test: 0.6132294	best: 0.6132294 (0)	total: 1.65ms	remaining: 163ms
1:	learn: 0.5377961	test: 0.5417650	best: 0.5417650 (1)	total: 4.03ms	remaining: 198ms
2:	learn: 0.4803441	test: 0.4876763	best: 0.4876763 (2)	total: 6.96ms	remaining: 225ms
3:	learn: 0.4317808	test: 0.4410278	best: 0.4410278 (3)	total: 9.52ms	remaining: 229ms
4:	learn: 0.3910635	test: 0.4015778	best: 0.4015778 (4)	total: 12.9ms	remaining: 246ms
5:	learn: 0.3535918	test: 0.3645358	best: 0.3645358 (5)	total: 15.2ms	remaining: 238ms
6:	learn: 0.3214948	test: 0.3339100	best: 0.3339100 (6)	total: 16.9ms	remaining: 224ms
7:	learn: 0.2923459	test: 0.3044973	best: 0.3044973 (7)	total: 17.8ms	remaining: 205ms
8:	learn: 0.2672539	test: 0.2792859	best: 0.2792859 (8)	total: 19.6ms	remaining: 198ms
9:	learn: 0.2472711	test: 0.2595177	best: 0.2595177 (9)	total: 23ms	remaining: 207ms
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87:	learn: 0.0278995	test: 0.0473391	best: 0.0473391 (87)	total: 201ms	remaining: 27.4ms
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93:	learn: 0.0268545	test: 0.0464419	best: 0.0464419 (93)	total: 214ms	remaining: 13.7ms
94:	learn: 0.0267118	test: 0.0463292	best: 0.0463292 (94)	total: 216ms	remaining: 11.4ms
95:	learn: 0.0265656	test: 0.0460783	best: 0.0460783 (95)	total: 218ms	remaining: 9.08ms
96:	learn: 0.0263553	test: 0.0459863	best: 0.0459863 (96)	total: 219ms	remaining: 6.79ms
97:	learn: 0.0261243	test: 0.0457736	best: 0.0457736 (97)	total: 221ms	remaining: 4.52ms
98:	learn: 0.0259720	test: 0.0458616	best: 0.0457736 (97)	total: 224ms	remaining: 2.26ms
99:	learn: 0.0257431	test: 0.0457901	best: 0.0457736 (97)	total: 226ms	remaining: 0us

bestTest = 0.04577362885
bestIteration = 97

Shrink model to first 98 iterations.
Best iteration: 97
Validation f1 score: 0.9215686274509803
Inner train shape: (2205, 17)
val shape: (551, 17)
Learning rate set to 0.104889
0:	learn: 0.6101039	test: 0.6094434	best: 0.6094434 (0)	total: 1.96ms	remaining: 194ms
1:	learn: 0.5379245	test: 0.5365366	best: 0.5365366 (1)	total: 4.18ms	remaining: 205ms
2:	learn: 0.4840307	test: 0.4824503	best: 0.4824503 (2)	total: 10.1ms	remaining: 326ms
3:	learn: 0.4345369	test: 0.4314916	best: 0.4314916 (3)	total: 12.4ms	remaining: 299ms
4:	learn: 0.3981978	test: 0.3950847	best: 0.3950847 (4)	total: 14.3ms	remaining: 273ms
5:	learn: 0.3614046	test: 0.3583213	best: 0.3583213 (5)	total: 15.9ms	remaining: 249ms
6:	learn: 0.3317677	test: 0.3277871	best: 0.3277871 (6)	total: 17.4ms	remaining: 231ms
7:	learn: 0.3029503	test: 0.2988676	best: 0.2988676 (7)	total: 19.3ms	remaining: 222ms
8:	learn: 0.2782628	test: 0.2746801	best: 0.2746801 (8)	total: 22.6ms	remaining: 228ms
9:	learn: 0.2565987	test: 0.2521344	best: 0.2521344 (9)	total: 25.2ms	remaining: 227ms
10:	learn: 0.2376618	test: 0.2336630	best: 0.2336630 (10)	total: 27.4ms	remaining: 222ms
11:	learn: 0.2214380	test: 0.2173049	best: 0.2173049 (11)	total: 29.6ms	remaining: 217ms
12:	learn: 0.2060016	test: 0.2022215	best: 0.2022215 (12)	total: 31.1ms	remaining: 208ms
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bestTest = 0.04466078667
bestIteration = 99

Best iteration: 99
Validation f1 score: 0.9215686274509804
Inner train shape: (2205, 17)
val shape: (551, 17)
Learning rate set to 0.104889
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bestTest = 0.03584421871
bestIteration = 99

Best iteration: 99
Validation f1 score: 0.9523809523809523
Average best iteration: 98.6
Scaled average best iteration: 123
Learning rate set to 0.108515
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Test f1 score: 0.9218749999999999

Compute average CatBoost performance on validation and test sets

avg_val_f1 = np.mean(stratified_kfold_results['val_f1'])
avg_test_f1 = np.mean(stratified_kfold_results['test_f1'])
avg_num_boost_rounds = int(np.mean(stratified_kfold_results['test_num_boost_rounds']))

print(f'Average validation f1: {avg_val_f1}')
print(f'Average test f1: {avg_test_f1}')
print(f'Average test num_boost_rounds: {avg_num_boost_rounds}')

Average validation f1: 0.9225705696238471
Average test f1: 0.9281793928719436
Average test num_boost_rounds: 123

Train CatBoost on full train dataset

model_path = './models/cb_model_final.cbm'
full_num_boost_rounds = int(np.mean(stratified_kfold_results['test_num_boost_rounds']) * scaling_factor)
print(f' Number of iterations used to train CatBoost model on full dataset: {full_num_boost_rounds}')
cb_model = train_catboost_model(
    train = train,
    num_boost_rounds = full_num_boost_rounds, 
    features = features, 
    cat_features = cat_features,
    model_path = model_path,
)

 Number of iterations used to train CatBoost model on full dataset: 153
Learning rate set to 0.097701
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Compute CatBoost performance on actual held out test dataset

test_preds = (cb_model.predict(test[features]) > threshold).astype(int)
cb_performance_dict = classification_report(test[label_col], test_preds, output_dict = True)['1']
cb_performance_dict['model'] = model_name

print(classification_report(test[label_col], test_preds))

              precision    recall  f1-score   support

           0       0.98      1.00      0.99      1332
           1       0.98      0.85      0.91       144

    accuracy                           0.98      1476
   macro avg       0.98      0.92      0.95      1476
weighted avg       0.98      0.98      0.98      1476

Model Evaluation

performance = {
    'model': [],
    'precision': [],
    'recall': [],
    'f1-score': [],
}

for k, v in performance.items():
    v.append(lgb_performance_dict[k])
    v.append(cb_performance_dict[k])

pd.DataFrame(performance)

	model	precision	recall	f1-score
0	lightgbm	0.937008	0.826389	0.878229
1	catboost	0.976000	0.847222	0.907063

From the table above, it could be observed that CatBoost outperforms LightGBM in all 3 metrics, precision, recall and f1-score. Hence, CatBoost is the recommended model to use for this problem.

Explain how different features impact model predictions with SHAP values

import shap

explainer = shap.TreeExplainer(cb_model)
shap_values = explainer(test[features])

IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html

Overall Feature Importance

max_display = 15
shap.plots.bar(shap_values, max_display = max_display)

From the plot above, it could be observed that the top 5 most influential features, in descending order are:

1. income
2. cc_avg_spending
3. education
4. family_size
5. income_cc_spending_diff

The bottom feature, postal_code is observed to have minimal to no effect on the model predictions.

Effect of feature values on model predictions

shap.summary_plot(shap_values)

No data for colormapping provided via 'c'. Parameters 'vmin', 'vmax' will be ignored

# Inverse transform education
encoder_dict['education'].inverse_transform([0, 1, 2])

array(['Advanced Degree', 'Graduate', 'Undergrad'], dtype=object)

From the plot above, it could be observed that in general, higher annual income, higher average monthly credit card spending, larger family size, greater difference between annual income and annual credit card spending and having a deposit account, have a positive impact on the model prediction output (i.e. increased probability of personal loan take up).

Conversely, having a lower annual income, lower average monthly credit card spending, smaller family size, smaller difference between annual income and annual credit card spending, not having a deposit account and being an 'Undergrad' have a negative impact on the model prediction output (i.e. decreased probability of personal loan take up).

Local contribution of feature values on model prediction for a single observation

Local contribution of features values on model prediction, personal_loan = 1 for a single observation

idx = test[test.personal_loan == 1].index[0]
print(test.iloc[idx])
shap.plots.waterfall(shap_values[idx], max_display = max_display)

id                          994.000000
age                          41.000000
experience                   15.000000
income                      185.000000
postal_code                 125.000000
family_size                   1.000000
cc_avg_spending               3.600000
education                     1.000000
mortgage                      0.000000
investment_account            0.000000
deposit_account               0.000000
internet_banking              0.000000
personal_loan                 1.000000
income_cc_spending_diff     141.800000
cc_spending_income_ratio      0.233514
mortage_income_diff        -185.000000
mortage_income_ratio          0.000000
Name: 13, dtype: float64

From the plot above, it could be observed that having a high annual income (i.e. 185K), high average monthly credit card spending (i.e. 3.6K) and being a 'Graduate' heavily influences the model to predict that the customer will take up the personal loan.

Local contribution of features values on model prediction, personal_loan = 0 for a single observation

idx = test[test.personal_loan == 0].index[-1]
print(test.iloc[idx])
shap.plots.waterfall(shap_values[idx], max_display = max_display)

id                          823.00
age                          61.00
experience                   35.00
income                       60.00
postal_code                 462.00
family_size                   3.00
cc_avg_spending               1.40
education                     0.00
mortgage                      0.00
investment_account            0.00
deposit_account               0.00
internet_banking              0.00
personal_loan                 0.00
income_cc_spending_diff      43.20
cc_spending_income_ratio      0.28
mortage_income_diff         -60.00
mortage_income_ratio          0.00
Name: 1475, dtype: float64

From the plot above, it could be observed that having a low annual income (i.e. 60K) and low average monthly credit card spending (i.e. 1.4K) influences the model to predict that the customer is very unlikely to take up the personal loan. Conversely, having an 'Advanced Degree', influences the model to predict that the customer is more likely to take up the personal loan.